SoroushVahidi/scidocs huggingface dataset:
Context-Awareness for Mobile Sensing: A Survey and Future Directions
(2016)

The evolution of smartphones together with increasing computational power has empowered developers to create innovative context-aware applications for recognizing user-related social and cognitive activities in any situation and at any location. The existence and awareness of the context provide the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze, and share local sensory knowledge in the purpose for a large-scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects and also assist individuals. However, many open challenges remain, which are mostly arisen because the middleware services provided in mobile devices have limited resources in terms of power, memory, and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved and, at the same time, better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlightens them by proposing possible solutions.
Recent advances in image processing techniques for automated harvesting purposes: A review
(2017)

Image processing has been proved to be an effective tool for analysis in various human activity areas, namely, agricultural applications. Interpreting a digital color image of fruit orchard captured in field environment is extremely challenging due to adverse weather conditions, luminance variability and the presence of dust, insects and other unavoidable image noises. The purpose of this survey is to categorize and briefly review the literature on computer analysis of fruit images in agricultural operations, which comprises more than 60 papers published in the last 10 years. With the aim to perform applied research in agricultural imaging, this paper intends to focus on advanced image processing and analysis techniques used in applications for detection and classifications of fruits, developed in the last decade. For the reviewed techniques, some performance evaluation metrics achieved in various experiments are emphasized to help the researchers when making choices and develop new computer vision applications in fruit images.
On-Line New Event Detection and Tracking
(1998)

We define and describe the related problems of new event detection and event tracking within a stream of broadcast news stories. We focus on a strict on-line setting-i.e., the system must make decisions about one story before looking at any subsequent stories. Our approach to detection uses a single pass clustering algorithm and a novel thresholding model that incorporates the properties of events as a major component. Our approach to tracking is similar to typical information filtering methods. We discuss the value of “surprising” features that have unusual occurrence characteristics, and briefly explore on-line adaptive filtering to handle evolving events in the news. New event detection and event tracking are part of the Topic Detection and Tracking (TDT) initiative.
Does visual attention select objects or locations?
(1994)

Much research supports location-based attentional selection, but J. Duncan (1984) presented data favoring object-based selection in a shape discrimination task. Does attention select objects or locations? We confirmed that Duncan's task elicits selection from spatially invariant object representations rather than from a grouped location-based representation. We next asked whether this finding was due to location-based filtering; the results again supported object-based selection. Finally, we demonstrated that when Duncan's objects were used in a cued detection task the results were consistent with location-based selection. These results suggest that there may not be a single attention mechanism, consistent with Duncan's original claim that object-based and location-based attentional selection are not mutually exclusive. Rather, attentional limitations may depend on the type of stimulus representation used in performing a given task.
The Genetic Structure of Marijuana and Hemp
(2015)

Despite its cultivation as a source of food, fibre and medicine, and its global status as the most used illicit drug, the genus Cannabis has an inconclusive taxonomic organization and evolutionary history. Drug types of Cannabis (marijuana), which contain high amounts of the psychoactive cannabinoid Δ9-tetrahydrocannabinol (THC), are used for medical purposes and as a recreational drug. Hemp types are grown for the production of seed and fibre, and contain low amounts of THC. Two species or gene pools (C. sativa and C. indica) are widely used in describing the pedigree or appearance of cultivated Cannabis plants. Using 14,031 single-nucleotide polymorphisms (SNPs) genotyped in 81 marijuana and 43 hemp samples, we show that marijuana and hemp are significantly differentiated at a genome-wide level, demonstrating that the distinction between these populations is not limited to genes underlying THC production. We find a moderate correlation between the genetic structure of marijuana strains and their reported C. sativa and C. indica ancestry and show that marijuana strain names often do not reflect a meaningful genetic identity. We also provide evidence that hemp is genetically more similar to C. indica type marijuana than to C. sativa strains.
Solutions to the GSM Security Weaknesses
(2008)

Recently, the mobile industry has experienced an extreme increment in number of its users. The GSM network with the greatest worldwide number of users succumbs to several security vulnerabilities. Although some of its security problems are addressed in its upper generations, there are still many operators using 2G systems. This paper briefly presents the most important security flaws of the GSM network and its transport channels. It also provides some practical solutions to improve the security of currently available 2G systems.
Performance on Raven ’ s Advanced Progressive Matrices by African , East Indian , and White engineering students in South Africa
(2003)

The hypothesis is tested that the Raven’s Advanced Progressive Matrices (APM) has the same construct validity in African university students as it does in non-African university students. Analyses were made of scores from 294 highly select 17–23-year-olds in the Faculties of Engineering and the Built Environment at the University of the Witwatersrand (187 Africans, 40 East Indians, 67 Whites; 70 women, 224 men). Out of a total of 36 problems, the African students solved an average of 22, the East Indian students, 25, and the White students, 29 (P < .001), placing them at the 57th, 64th, and 86th percentiles, respectively, and yielding IQ equivalents of 103, 106, and 117 on the 1993 US norms. Four months earlier, they had completed the Standard Progressive Matrices. The two tests correlated .60 or higher for both the Africans and the non-Africans, and both tests predicted final end-of-year grades with mean r’s = .30 (P’s < .05). Items found difficult by one group were difficult for the others; items found easy by one group were easy for the others (mean r’s = .90, P < .001). The African–East Indian–White differences were ‘‘Jensen Effects,’’ being most pronounced on the general factor of intelligence (measured in this instance by items with the highest item-total correlations). Indeed, the g loadings showed cross-cultural generality: For example, item-total correlations calculated on the East Indian students predicted the magnitude of the African–White differences. When each of the 0160-2896/03/$ – see front matter D 2003 Elsevier Science Inc. All rights reserved. PII: S0160 -2896 (02 )00140 -X * Corresponding author. Tel.: +1-519-661-3685; fax: +1-519-850-2302. E-mail addresses: Rushton@uwo.ca (J.P. Rushton), 135skuy@mentor.edcm.wits.ac.za (M. Skuy). 1 Tel.: +27-11-716-5286; fax: +27-11-339-3844. Intelligence 31 (2003) 123–137 36 g loadings and race effects were aggregated into nine four-item ‘‘subtests,’’ the magnitude of the Jensen Effect was Spearman’s r = .52. There were no sex differences. D 2003 Elsevier Science Inc. All rights reserved.
THRIVE: Threshold Homomorphic encryption based secure and privacy preserving bIometric VErification system
(2015)

In this paper, we introduce a new biometric verification and template protection system which we call THRIVE. The system includes novel enrollment and authentication protocols based on threshold homomorphic encryption where a private key is shared between a user and a verifier. In the THRIVE system, only encrypted binary biometric templates are stored in a database and verification is performed via homomorphically randomized templates, thus, original templates are never revealed during authentication. Due to the underlying threshold homomorphic encryption scheme, a malicious database owner cannot perform full decryption on encrypted templates of the users in the database. In addition, security of the THRIVE system is enhanced using a two-factor authentication scheme involving user’s private key and biometric data. Using simulation-based techniques, the proposed system is proven secure in the malicious model. The proposed system is suitable for applications where the user does not want to reveal her biometrics to the verifier in plain form, but needs to prove her identity by using biometrics. The system can be used with any biometric modality where a feature extraction method yields a fixed size binary template and a query template is verified when its Hamming distance to the database template is less than a threshold. The overall connection time for the proposed THRIVE system is estimated to be 336 ms on average for 256-bit biometric templates on a desktop PC running with quad core 3.2 GHz CPUs at 10 Mbit/s up/down link connection speed. Consequently, the proposed system can be efficiently used in real-life applications.
Key performance indicators for traffic intensive web-enabled business processes
(2012)

Purpose – Intensive traffic often occurs in web-enabled business processes hosted by travel industry and government portals. An extreme case for intensive traffic is flash crowd situations when the number of web users spike within a short time due to unexpected events caused by political unrest or extreme weather conditions. As a result, the servers hosting these business processes can no longer handle overwhelming service requests. To alleviate this problem, process engineers usually analyze audit trail data collected from the application server and reengineer their business processes to withstand unexpected surge in the visitors. However, such analysis can only reveal the performance of the application server from the internal perspective. This paper aims to investigate this issue. Design/methodology/approach – This paper proposes an approach for analyzing key performance indicators of traffic intensive web-enabled business processes from audit trail data, web server logs, and stress testing logs. Findings – The key performance indicators identified in the study’s approach can be used to understand the behavior of traffic intensive web-enabled business processes and the underlying factors that affect the stability of the web server. Originality/value – The proposed analysis also provides an internal as well as an external view of the performance. Moreover, the calculated key performance indicators can be used by the process engineers for locating potential bottlenecks, reengineering business processes, and implementing contingency measures for traffic intensive situations.
MobiLearn go: mobile microlearning as an active, location-aware game
(2017)

Mobile technologies hold great potential to make studying both more effective and more enjoyable. In this work we present a mobile, microlearning application. Our system is designed with two goals: be flexible enough to support learning in any subject and encourage frequent short study sessions in a variety of contexts. We discuss the use of our application to assess the feasibility of microlearning for non-language learning and the relationship between the physical location of study sessions and information retention.
Clustering with Deep Learning: Taxonomy and New Methods
(2018)

Clustering methods based on deep neural networks have proven promising for clustering real-world data because of their high representational power. In this paper we propose a systematic taxonomy of clustering methods that utilize deep neural networks. We base our taxonomy on a comprehensive review of recent work and validate the taxonomy in a case study. In this case study, we show that the taxonomy enables researchers and practitioners to systematically create new clustering methods by selectively recombining and replacing distinct aspects of previous methods with the goal of overcoming their individual limitations. The experimental evaluation confirms this and shows that the method created for the case study achieves state-of-the-art clustering quality and surpasses it in some cases.
Color-Ciratefi : A color-based RST-invariant template matching algorithm
(2010)

Template matching is a technique widely used for finding patterns in digital images. An efficient template matching algorithm should be able to detect template instances that have undergone geometric transformations. Similarly, a color template matching should be able to deal with color constancy problem. Recently we have proposed a new grayscale template matching algorithm named Ciratefi, invariant to rotation, scale, translation, brightness and contrast. In this paper we introduce the Color-Ciratefi that takes into account the color information. We use a new similarity metric in the CIELAB space to obtain invariance to brightness and contrast changes. Experiments show that Color-Ciratefi is more accurate than C-color-SIFT, the well-known SIFT algorithm that uses a set of color invariants. In conventional computers, Color-Ciratefi is slower than C-color-SIFT. However Color-Ciratefi is more suitable than C-color-SIFT to be implemented in highly parallel architectures like FPGA, because it repeats exactly the same set of operations for each pixel.
The Faces of Engagement: Automatic Recognition of Student Engagementfrom Facial Expressions
(2014)

Student engagement is a key concept in contemporary education, where it is valued as a goal in its own right. In this paper we explore approaches for automatic recognition of engagement from students' facial expressions. We studied whether human observers can reliably judge engagement from the face; analyzed the signals observers use to make these judgments; and automated the process using machine learning. We found that human observers reliably agree when discriminating low versus high degrees of engagement (Cohen's κ = 0.96). When fine discrimination is required (four distinct levels) the reliability decreases, but is still quite high ( κ = 0.56). Furthermore, we found that engagement labels of 10-second video clips can be reliably predicted from the average labels of their constituent frames (Pearson r=0.85), suggesting that static expressions contain the bulk of the information used by observers. We used machine learning to develop automatic engagement detectors and found that for binary classification (e.g., high engagement versus low engagement), automated engagement detectors perform with comparable accuracy to humans. Finally, we show that both human and automatic engagement judgments correlate with task performance. In our experiment, student post-test performance was predicted with comparable accuracy from engagement labels ( r=0.47) as from pre-test scores ( r=0.44).
Image Thresholding for Optical Character Recognition and Other Applications Requiring Character Image Extraction
(1983)

Two new, cost-effective thresholding algorithms for use in extracting binary images of characters from machineor hand-printed documents are described. The creation of a binary representation from an analog image requires such algorithms to determine whether a point is converted into a binary one because it falls within a character stroke or a binary zero because it does not. This thresholding is a critical step in Optical Character Recognition (OCR). I t is also essential for other Character Image Extraction (CIEJ applications, such as the processing of machine-printed or handwritten characters from carbon copy forms or bank checks, where smudges and scenic backgrounds, for example, may have to be suppressed. The first algorithm, a nonlinear, adaptive procedure, is implemented with a minimum of hardware and is intended for many CIE applications. The second is a more aggressive approach directed toward specialized, high-volume applications which justify extra complexity.
Neuropsychologic Theory and Findings in Attention-Deficit/Hyperactivity Disorder: The State of the Field and Salient Challenges for the Coming Decade
(2005)

The past decade has witnessed the establishment of several now well-replicated findings in the neuropsychology of attention-deficit/hyperactivity disorder (ADHD), which have been confirmed by meta-analyses. Progress has been notable from the importing of cognitive science and neuroscience paradigms. Yet these findings point to many neural networks being involved in the syndrome and to modest effect sizes suggesting that any one neuropsychologic deficit will not be able to explain the disorder. In this article, leading theories and key findings are briefly reviewed in four key domains: attention, executive functions, state regulation and motivation, and temporal information processing. Key issues facing the field of neuropsychologic research and theory in ADHD include 1) the need for more integrative developmental accounts that address both multiple neural systems and the socialization processes that assure their development; 2) consideration of multiple models/measures in the same study so as to examine relative contributions, within-group heterogeneity, and differential deficit; and 3) better integration of cognitive process models with affective and temperament theories so that early precursors to ADHD can be better understood. Overall, the field has witnessed notable progress as it converges on an understanding of ADHD in relation to disruption of a multicomponent self-regulatory system. The next era must articulate multipathway, multilevel developmental accounts of ADHD that incorporate neuropsychologic effects.
Multimodal Deep Learning
(2011)

Deep networks have been successfully applied to unsupervised feature learning for single modalities (e.g., text, images or audio). In this work, we propose a novel application of deep networks to learn features over multiple modalities. We present a series of tasks for multimodal learning and show how to train a deep network that learns features to address these tasks. In particular, we demonstrate cross modality feature learning, where better features for one modality (e.g., video) can be learned if multiple modalities (e.g., audio and video) are present at feature learning time. Furthermore, we show how to learn a shared representation between modalities and evaluate it on a unique task, where the classifier is trained with audio-only data but tested with video-only data and vice-versa. We validate our methods on the CUAVE and AVLetters datasets with an audio-visual speech classification task, demonstrating superior visual speech classification on AVLetters and effective multimodal fusion.
Transportation mode detection using mobile phones and GIS information
(2011)

The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.
Power Consumption Modeling of Skid-Steer Tracked Mobile Robots on Rigid Terrain
(2009)

Power consumption is a key element in outdoor mobile robot autonomy. This issue is very relevant in skid-steer tracked vehicles on account of their large ground contact area. In this paper, the power losses due to dynamic friction have been modeled from two different perspectives: 1) the power drawn by the rigid terrain and 2) the power supplied by the motors. Comparison of both approaches has provided new insight on skid steering on hard flat terrains at walking speeds. Experimental power models, which also include traction resistance and other power losses, have been obtained for two different track widths over marble flooring and asphalt with Auriga- beta, which is a full-size mobile robot. To this end, various internal probes have been set at different points of the power stream. Furthermore, new energy implications for navigation of these kinds of vehicles have been deduced and tested.
Putting value co-creation into practice: a case for advisory support
(2011)

The concept of value co-creation and its notion of the customer as co-creator of value have gained much academic interest, notably in marketing and operations research. While several competing perspectives have been conceptually discussed in literature, research on the practical implications of value co-creation is scarce. Using the example of sales-oriented advisory, we show gaps between existing co-creation concepts and current practice in five problem areas. We develop four general solution perspectives on the advisor-client encounter as guidelines to overcome these gaps and discuss design requirements of their technological instantiations in advisory support systems. We present exemplary implementations of such systems in two domains: travel counseling and financial advisory. Revealing the practical implications of value co-creation on advisory encounters, these examples also demonstrate that the solution perspectives have to be implemented quite differently for individual domains.
Intrusion detection system for high-speed network
(2004)

The increasing network throughput challenges the current Network Intrusion Detection Systems (NIDS) to have compatible highperformance data processing. In this paper, we describe an in-depth research on the related techniques of high-performance network intrusion detection and an implementation of a Rule-based High-performance Network Intrusion Detection System (RHPNIDS) for high-speed networks. By integrating several performance optimizing methods, the performance of RHPNIDS is very impressive compared with the popular open source NIDS Snort. q 2004 Elsevier B.V. All rights reserved.